Senior Ontologist - Knowledge Graph & Identity

Sambatv
Warsaw, Poland
Hybrid

Who this role is best for

Media domain experts with semantic modeling experience will find this ontology-focused role in Warsaw, emphasizing cross-functional collaboration and AI integration.

Best fit for

  • Candidates with 5–8 years of ontology engineering experience and a background in media or ad tech
    — “Domain knowledge in media, entertainment, or ad tech
  • Individuals who have led ontology design reviews and balanced trade-offs between expressivity and scalability
    — “articulating trade-offs between expressivity, scalability, and query performance clearly
  • Professionals with production-level knowledge of RDF, OWL, and SPARQL, and a track record of building large-scale ontologies
    — “demonstrable track record of production ontologies at scale

Things to consider

  • The role demands formal mentorship and internal technical leadership responsibilities
    — “Formally mentor Ontology Engineers and junior data scientists
  • Candidates must be comfortable with embedding models and vector databases
    — “Experience with embedding models, vector databases

How to stand out

  • Highlight your experience with entity resolution and data pipeline design in your resume and interviews
    — “Hands-on experience with entity resolution, record linkage, or deduplication at scale
  • Emphasize your ability to explain complex semantic modeling trade-offs to non-specialists
    — “Strong communicator - able to defend ontological modeling decisions in design reviews
  • Showcase your work with production triplestores like Amazon Neptune or Stardog
    — “production triplestore (Amazon Neptune, Stardog, GraphDB, Jena, or equivalent)
Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • design, development, and versioning of core ontologies
  • author and maintain SHACL shapes
  • define and document derived-attribute schemas
  • establish ontology design standards
  • lead ontology design reviews
Typical background
5–8 years of hands-on experience in ontology engineering, semantic data modeling

Skills & requirements

Required

Ontology EngineeringSemantic Data ModelingRdf/rdfs/owlShaclPythonSparqlData EngineeringEntity ResolutionRecord LinkageKnowledge Graph DevelopmentAI IntegrationCross-functional CollaborationMentorship

Preferred

Llm-augmented ApproachesEmbedding-based Approaches

Stack & domain

RDFRDFSOWLShaclPythonSparqlDatabricksSparkLLMEmbedding-basedEntity ResolutionRecord LinkageSemantic SimilarityVector StoreGraphragAIIdentity SecurityCloud-nativeCentralized AuthorizationContextIdentity LifecycleCloudTraditional InfrastructureDataSaas ApplicationsDiscoveryAccess LevelsIrregularitiesThreatsReal-timeCloud-native Identity Security PlatformCollaborationMentoringTechnical TalksWorkshopsSemantic ModelingShacl Design PatternsGraph Best PracticesMedia IntelligenceConsumer AttentionData ModelingKnowledge GraphSemantic WebOntology

About the role

Original posting from Sambatv via Lever

Samba is a media intelligence company. We know what the world is watching, reading, and thinking about — in real time, at scale, across every screen. Our data exists with the consent of over a billion people, organized into the most complete picture of consumer attention ever built. The biggest brands in the world use that picture to make smarter decisions. We think it’s the most interesting data asset on the planet, because it’s the most culturally relevant. 

What You'll Do:

Ontology Design & Governance

Own the end-to-end design, development, and versioning of Samba TV's core ontologies in RDF/RDFS/OWL - defining entity classes, properties, hierarchies, and constraints that accurately model Samba's data domain at scale

Author and maintain SHACL shapes for post-load graph validation, consistency checking, and data quality enforcement

Define and document derived-attribute schemas - genre affinity, brand affinity, topic affinity, lifecycle signals, and viewing summaries - and own the logical definitions that govern how raw events become durable graph attributes

Establish ontology design standards, change management processes, and versioning practices; evaluate alignment with W3C standards and relevant industry schemas (Schema.org, EIDR, DDEX, W3C PROV)

Lead ontology design reviews with product, data engineering, and data science stakeholders - articulating trade-offs between expressivity, scalability, and query performance clearly

Event-to-Ontology Derivation

Define the aggregation and scoring logic that transforms raw TV viewership and web activity events into the durable affinities, summaries, and inferred signals that live in the graph

Co-own derivation pipeline design with data engineering - specifying transformation logic, intermediate schemas, and validation checkpoints for Databricks/Spark pipelines that feed the materialized graph substrate

Reason carefully about what belongs in the graph vs. what should remain virtualized in the data lake - balancing query performance against storage and refresh cost

Knowledge Graph Development & AI Integration

Build and maintain production-quality knowledge graph pipelines in Python and SPARQL - well-tested, documented, and scalable to Samba's data volumes

Design and implement entity resolution and record linkage pipelines that map real-world entities (content titles, devices, audiences, advertisers) to canonical knowledge graph nodes

Develop enrichment workflows that integrate third-party data sources (metadata providers, identity vendors, web sources) into Samba's knowledge graph in a consistent, governed way

Apply embedding-based and LLM-augmented approaches to ontology mapping, entity disambiguation, and semantic similarity problems

Support content and semantic embedding pipelines that feed into the vector store and underpin GraphRAG-based AI solutions

Cross-functional Collaboration & Mentorship

Partner with data engineering and platform teams to ensure the knowledge graph is integrated, queryable, and production-ready at scale

Collaborate with product to translate business requirements into ontological and graph data model decisions

Formally mentor Ontology Engineers and junior data scientists on semantic modeling, SHACL design patterns, and graph best practices

Lead internal technical talks and workshops on ontology, knowledge graph, and semantic web topics

Who You Are:

Must-Haves

5–8 years of hands-on experience in ontology engineering, semantic data modeling, or knowledge graph development - with a demonstrable track record of production ontologies at scale

Deep expertise in W3C semantic web standards: RDF, RDFS, OWL, SPARQL 1.1, and SHACL - with hands-on experience building and validating graph schemas in a production triplestore (Amazon Neptune, Stardog, GraphDB, Jena, or equivalent)

Strong Python - production-quality, well-tested code; comfortable building data pipelines and graph processing workflows

First-principles understanding of description logics, ontology design patterns, and the practical trade-offs between OWL expressivity and triplestore scalability

Hands-on experience with entity resolution, record linkage, or deduplication at scale - mapping messy, multi-source real-world data to clean ontological representations

Bachelor's degree required in Computer Science, Information Science, Computational Linguistics, Mathematics, or a related field; Master's or PhD strongly preferred

Strong communicator - able to defend ontological modeling decisions in design reviews and explain trade-offs to non-specialist stakeholders

Strongly Preferred

Hands-on experience with Amazon Neptune or Stardog - including data virtualization (Neptune Orion or Stardog Virtual Graphs) over data lake sources

Experience designing aggregation and derivation logic that converts raw behavioral event data into durable, graph-resident derived attributes

Domain knowledge in media, entertainment, or ad tech - TV viewership (ACR/STB), digital audience modeling (device graphs, identity resolution), or ad exposure data

Familiarity with industry content and identity schemas: EIDR, Schema.org VideoObject, DDEX, or equivalent

Experience with embedding models, vector databases (Milvus, Pinecone, Weaviate), and GraphRAG architectures (LangChain/LlamaIndex)

Familiarity with GNN-based approaches to knowledge graph reasoning or entity resolution a plus

Working knowledge of PySpark and Databricks for large-scale transformation pipelines

Samba is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.  We strive to empower connection with one another, reflect the communities we serve, and tackle meaningful projects that make a real impact.

Samba may collect personal information directly from you, as a job applicant, Samba may also receive personal information from third parties, for example, in connection with a background, employment or reference check, in accordance with the applicable law. For further details, please see Samba's Applicant Privacy Policy. For residents of the EU , Samba Inc. is the data controller.

Source: Sambatv careers (Lever)

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